CSR GRACE & GRACE-FO Dynamic Ocean Mascons RL06.2DO
Bibliographic record
Abstract
RL06.2DO monthly mass anomaly grids from GRACE and GRACE-FO determined following CSR RL06.2 processing altered for Dynamic Ocean analysis. GAD-based regularization constraints are used over the Arctic Ocean. The mascon processing includes a specific handling of the major earthquakes in Japan and Andaman Bay, similar to what was done for the RL06.2EQ mascons (doi:10.18738/T8/ZE7DUD), with a model of the Earthquake signals removed from the mascons in those regions. Additionally a GRD model is computed using the mascon ocean mask and consistent with Tamisiea et al., 2010, (doi:10.1029/2009JC005687) and removed from the mascons. The Earthquake and GRD models are provided as a companion grids, but both signals are already corrected for in the RL06.2DO mascons. All grids are provided globally with a quarter degree sampling in longitude/latitude. Only the ocean mascons are reported. The land mascons are set to "-99999.0". The grids cover the GRACE and GRACE-FO period from 04/2002 to 05/2024
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.071 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".